#341 Celia Merzbacher: Beyond the Buzzword: The Real State of Quantum Computing, Sensing, and AI in 2025

[00:00:00] Celia Merzbacher Hi there, I'm Celia Merzbacher. I'm Executive Director of the Quantum Economic Development Consortium or QEDC. QEDC is an unusual public-private partnership of sorts that was called for by Congress in the National Quantum Initiative Act signed in 2018 by then President Trump. And in that act, Congress directed the Department of Commerce and in particular NIST, the National Institute of Standards and Technology, to establish a consortium of stakeholders.

[00:00:27] And I think that was an intentionally broad term. And the purpose of this consortium was to gather and build a community of stakeholders were going to be essential to realizing the benefits and the economic value of quantum technologies. The U.S. government was about to make some really big investments on the government side to help to fund the research that needed to be done.

[00:00:50] But they already were, like all of us, reading the headlines that said companies were building quantum computers and they were going to be here soon. And so they created, they decided they sort of needed an organization that would be a bridge between the private sector and the public sector. And that's what QEDC is. And here we are about seven years later now and or more. And we've built a community, a consortium. It's a member-based organization. We have about nearly 250 members, non-government members.

[00:01:20] We also allow government to participate and really get the benefits of membership without having to sign any membership agreements. So we have a lot of opportunities for government and industry to interact and connect and talk about what are the gaps and who should fill them. QEDC was stood up and we were directed to identify gaps that needed to be filled and strategies for filling those gaps. But we weren't given a big checkbook to write the checks to fill the gaps.

[00:01:48] So we really rely on partnership with government agencies and sometimes the private sector to fill the gaps and allow everyone to run faster. And we have members from across the ecosystem, from small startups making small components and critical enabling technologies to the big deep tech companies that are building systems and moving the products ahead that are going to be in not just the quantum computing space, but also quantum sensing, quantum communications and so on.

[00:02:18] So quantum is a very broad platform type of technology. And I look forward to jumping into that with you today, Craig.

[00:02:53] Yeah. We're around a billion dollars and most certainly will grow exponentially if quantum computing becomes practical. What do you learn from your survey in particular? How are companies preparing for the quantum age? Thanks. Yeah, that is a study, a survey based report that we've done now for five years. And we indeed are putting together our annual update.

[00:03:21] We expect to release it around World Quantum Day, April 14th. And so that's something that folks can look for coming soon. But each year we do ask and collect information on revenues. So we're able to sort of see those trends in the growth of sales year over year. And despite the fact that revenues have been steadily increasing about 27 percent each year, year over year.

[00:03:45] And interestingly, the actual numbers that we get in any given year exceed the amounts that were estimated the year before for that year. So there's sort of is a conservative estimate that we're picking up with our survey. And the market, in fact, is growing faster than expected by the companies that are in the business. So there's growing revenues. I will say that that doesn't mean that these companies are profitable.

[00:04:10] They're still spending a lot of money to advance the state of the art and the technology, of course. But we ask a lot of other questions. And if the viewers go to our website, you can easily find last year's report and see the sort of deep dive that we do beyond just the market numbers, the revenue numbers. We ask, do you foresee that most of the quantum computing will be accessed by the cloud or on-prem installations?

[00:04:36] And that on-prem sector has been growing, interestingly, rather quickly year over year. We ask questions about which end users are most likely to be the first to adopt quantum computing in their business. And that shifts a little bit year over year between energy, defense, research, pharmaceutical or finance, for instance. So these are all sort of snapshots that we get each year on where the market is and where the companies see it heading.

[00:05:04] We also ask a question, do you foresee a quantum winter? This is a concern, of course, for investors and others. And there have been some trends on that. So whereas there's a lot of optimism about companies regarding their own revenue forecast, they are still conservative and they're paying attention to the potential of what we call a quantum winter. That is a sort of pullback from the investor community.

[00:05:29] I think one reason that that is unlikely, and this is my own opinion, is that quantum is getting a lot of government investment. And that's partly because there are national security and government uses that are appreciated and important. And therefore, they perhaps are a little bit more buffered from the winds of the investor, the private investor community. So there's both the public investor trends and the private, and we're watching those closely.

[00:05:57] And I will say that this quantum market forecast that we do, we did quantum computing for several years. Last year, for the first time, we did a quantum sensing market forecast, and you can find that on our website as well. And finally, we have a sort of umbrella report that we did for the first time last year called the State of the Global Quantum Industry. And that goes beyond these revenue numbers, but looks at workforce and where are the companies located around the world and so on. So these are a number of different reports that QEDC puts out each year.

[00:06:26] This episode is brought to you by Tasty Trade. On Eye on AI, we talk a lot about how artificial intelligence is changing how people analyze information, spot patterns, and make more informed decisions. Markets are no different. The edge increasingly comes from having the right tools, the right data, and the ability to understand risk clearly.

[00:06:52] That's one of the reasons I like what Tasty Trade is building. With Tasty Trade, you can trade stocks, options, futures, and crypto all in one platform with low commissions, including zero commissions on stocks and crypto, so you keep more of what you earn.

[00:07:13] The platform is packed with advanced charting tools, backtesting, strategy selection, and risk analysis tools that help you think in probabilities rather than guesses. They've also introduced an AI-powered search feature that can help you discover symbols aligned with your interests, which is a smart way to explore markets more intentionally.

[00:07:38] For active traders, there are tools like Active Trader Mode, one-click trading, and smart order tracking. And if you're still learning, Tasty Trade offers dozens of free educational courses, plus live support from their trade desk reps during trading hours. If you're serious about trading in a world increasingly shaped by technology, check out Tasty Trade.

[00:08:08] Visit tastytrade.com to start your trading journey today. I'm going to myself. Tasty Trade Inc. is a registered broker-dealer and member of FINLA, NFA, and SIPC. Yeah. And when you talk about revenue, what areas are actually generating revenue?

[00:08:31] I know, for example, D-Wave has been selling quantum computers, annealing computers, which is kind of a different flavor than a lot of the companies pursuing. But where is the revenue coming from? I think it's mostly today coming from what you might call quantum computing as a service or consulting type business by these companies.

[00:08:54] And that's everything from D-Wave, Google, IBM, and others, and even smaller companies, pure play quantum companies like Continuum, INQ, Quera, and others. And they often are working with customers to give them the sort of inside tech support to appreciate what quantum computing could do for them. So while they're not all selling, although some of those companies are also selling quantum computer systems around the world,

[00:09:20] they also are just collaborating in a way with companies who, in the course of having those conversations, not only does the potential customer gain an understanding of how the quantum computing might impact their business, but the developer is getting insight into what customer needs are. So it's a very beneficial kind of collaboration, almost beyond consulting type relationships. Yeah, I was just over to IBM's Yorktown Research to talk to them about quantum.

[00:09:49] They have a very aggressive timeline. Would you say that they're ahead of others in the field? Or how do you see the hardware and software producers? What kind of a timeline do you look at? So I don't have particular insight. And I know you've got a lot of investors out there in your audience. I'm not going to give them investment advice. Probably placing bets on a number of different horses in this race might be the best strategy.

[00:10:19] I see companies like IBM and others making steady progress. They report updates against their roadmap. They're clearly marching along, making the engineering breakthroughs, advances that are needed in order to be able to scale things up. And they're not the only one. And so it is a little bit like betting at the beginning of a marathon right now to some extent, even though there is a lot of expectation that the advances that are being made will lead to what is sometimes called a utility scale quantum computer,

[00:10:49] something that can do something that's of business value in the next few years. Getting to a general purpose, really super powerful quantum computer that can do anything for anyone is a much longer term proposition. And so that's why sometimes you hear these wildly different numbers out there, because they're really talking about different capabilities in a way.

[00:11:10] And I think IBM's aggressive sounding timeline is we're going to have something that's really useful for something to customers within the next few years. And I think that is a refrain I'm hearing constantly. I was visiting a quantum computing company just yesterday, and the person who was hosting the meeting or the visit said 10 years ago or five years ago, I would have said it was going to be a much longer horizon timeline. And so everybody is seeing acceleration in progress right now.

[00:11:38] Yeah, I mean, there are some companies or some industries that are going to be more impacted than others. In last year's report, you talked about computational chemistry and pharmaceuticals emerging as really strong use cases. Why those sectors and what are they doing with quantum that's real today versus speculative? So there are a couple of ways of thinking about use cases.

[00:12:08] And at a very high level, you think for pharmaceuticals, drug discovery or materials for batteries or other green technology solutions, or for some kind of a defense or military aerospace type application or in the finance sector. So these are sector specific use cases. But if you kind of look at the type of problem that those sectors are trying to address, they often have some common mathematical requirements, right?

[00:12:36] They're based on some algorithm that has to do with optimization or solving some set of differential equations or worrying about complex problems like fluid flow, which is computationally very hard to model. And those types of problems are being studied and researched by all of the quantum computer developers and by academic and other scientists.

[00:12:58] So when there are breakthroughs made in being able to solve a particular class of algorithm or mathematical problem, it's likely to have utility in several different sectors. But companies, the businesses like pharmaceuticals are sort of an obvious one because they invest a lot of their revenues in research.

[00:13:18] And so whereas some industries have much smaller R&D budgets overall, the ones that are really dependent on infusions of technology and advances in things like being able to model new materials like drugs are the ones that are likely to make some of those investments themselves. I will say that we had a workshop recently where we invited someone from the oil and gas industry who explained how important high performance computing is for that industry.

[00:13:45] One that you think of as being very mature and perhaps not so quick to adopt new technologies, but in fact, high performance computation, which quantum is expected to enhance. So maybe we'll get into it, but really, it's not just about we'll use a quantum computer instead of a classical computer. In fact, it's likely to be hybrid computers where bringing them together will really allow for new types of computation.

[00:14:09] And the oil and gas industry does everything from having to sort of invert seismic information to maps of what's underground so they know where to drill to, you know, refinery processes, which are really chemistry problems. And so they have many kinds of problems that they would use quantum computing to address in that industry, for example. Yeah. And you mentioned that last year you had a report on quantum sensing.

[00:14:34] Can you tell us what quantum sensing is and why that market's growing? Yes. So quantum, let me just take one step back and say quantum is quantum. It's not just about computing. It is a field of physics.

[00:14:48] It's been around for 100 years or more. And in some ways, and I didn't give my own background, but in a past life, I was leading the U.S. government's national nanotechnology initiative, which the quantum initiative is very similar to the way that they approached advancing nanotechnology and involved multiple agencies and so on. And nanotechnology was about the ability to control matter at the nanoscale, at the scale of atoms. And that was just new 20, 25 years ago.

[00:15:15] And that ability is really critical to being able to actually take our understanding of quantum physics and the theory and making it real, because now we can control and manipulate things at the scale of atoms. And the quantum behavior of matter is sort of physics of quantum is becoming better and better known. And that's why we're investing so much in the research. Still, there's a lot of advances to be made.

[00:15:38] But quantum states of matter are inherently rather fragile and sensitive to the environment, which is the definition of a sensor, frankly. And in fact, in the case of quantum computers, as you saw when you went to the IBM Yorktown Heights facility, they have this big apparatus that they put the quantum computer inside because they are trying to keep it away from even thermal noise and other types of noise because that can shorten the computation time that you have to work with. And so quantum sensors take advantage of this feature, you could say.

[00:16:07] And you can measure all kinds of things that are in the environment, whether it's electric fields or motion or gravity and so on. So there are a lot of different kinds of quantum based sensors out there that are, in fact, rather closer and in some cases even already on the market. They have this very high sensitivity. Sometimes they have to be protected from other signals in the environment. There are some interesting biomedical imaging applications, for instance, that are in clinical studies now.

[00:16:36] So quantum sensing is a whole other area of application of our understanding and ability to control quantum states of matter. Yeah. And just so I understand, quantum sensing, it's not sensors being applied to quantum particles or quantum level phenomena. It's using quantum as a sensor, using quantum artifacts or particles as sensors. That's interesting.

[00:17:04] And that's, I think last year you said it's $300 or $400 million market. But how do you see that growing? It looks like it's growing kind of at a similar rate to the quantum computing market on the order of 25 to 30 percent year over year. It's somewhat more fragmented in a way, and it's ultimately maybe not going to be as large as quantum computing. So I think from the investor view, there's still this intense interest in quantum computing and its market growth.

[00:17:33] But quantum sensors, if you include quantum-based clocks or timekeeping systems, which are the basis of GPS and very important technologies that we use every day in all kinds of ways, not just for navigation and moving around, but for timestamping business transactions, for instance. So the ability to keep track of time is very important to business. It's a fundamental kind of technology that's widely used.

[00:18:02] And quantum is really at the heart of that. So if you include that kind of application, the market is quite large, potentially. The ability to not be at the sort of mercy of satellite technologies. The government, of course, the military is interested in navigating in areas where GPS isn't working very well or is maybe being jammed, for instance.

[00:18:23] So there are a lot of applications of quantum-based clocks, which is one kind of sensor and is a fairly substantial market today. Hmm, that's interesting. Given that for all the optimism, there is no practical quantum computer, a lot of the work is in designing algorithms for when those computers are eventually built and then using classical computers to run those algorithms.

[00:18:52] There are practical uses for those algorithms running on classical computers. Is that right? So there are what are known as quantum simulators, classical computers that can simulate a quantum computer. But because they're inherently limited by what a classical computer can do, they're really still being used more for research, I would say. And that's very useful and is sometimes easier to do a simulation than it is to even run your algorithm on a quantum computer today.

[00:19:21] So that's very helpful at this stage. Ultimately, the power of a quantum computer is going to be when you can use the algorithm on the quantum computer. I think you've, I really appreciate that you're mentioning the need for quantum algorithms. There are sort of three pillars of the stool that I think of. One is the quantum hardware, and we're watching that, you know, steadily improve and increase and scale up.

[00:19:44] There is a need because quantum, as I mentioned, this whole idea that quantum is sort of inherently sensitive makes it noisy and error prone. And so while the people who are making the hardware try to improve the noisiness of the system and decrease the error rate, there's a need to have error correction. And so that's another area where there's being a lot of work is being done and progress is being made. And so that's necessary and happening. And then the third sort of pillar is the algorithms.

[00:20:12] You need to have some software to run on a quantum computer. And algorithm development is an area where I think there's a lot going on, but there could be more. And my belief, and this is Celia speaking as a once upon a time scientist. And when I was doing research in the lab and a new capability or tool came along, I would very quickly take that into my lab and start using it to experiment with.

[00:20:38] And I didn't care if it was a little bit flaky, you know, and you got the blue screen of death or something and you had to reboot. That's OK. You're just doing a science experiment. You reboot and you keep going. But, you know, if you're going to deploy an application as your inventory control system at Walmart or some, you know, major energy grid management tool, that needs to be very robust and stable and reliable. And so I think we're going to get to this scientific experimentation stage very soon.

[00:21:06] As soon as scientists see a tool that's useful, they're going to start experimenting with it. And I think algorithms and other advances will be good. There'll be a sort of proliferation at that stage. It'll be really the chat GPT moment in some ways because people will just start using it for all kinds of things. And then that will lead to while they're maybe doing it for their scientific research, I think that will spill over very quickly and be embraced in commercial applications as well. So that's kind of what I'm expecting in the next few years.

[00:21:35] Yeah, I keep seeing, you know, as a journalist, I get pitch stuff all the time. I keep seeing people claiming that they've used a quantum computer, at least a quantum algorithm for to solve some optimization problem. Are there practical applications at this point? Or is this people slapping the word quantum on something? Well, yeah. And I can't speak to any particular example.

[00:22:05] But what I think we're in this phase now where there's very select problems that are being used to demonstrate. And sometimes they're curated in such a way that they, you know, are are kept from kind of going off into a corner. Or, well, I think they're very helpful in demonstrating the potential. They're less general purpose than what's ultimately going to be needed. And so I think that it's very exciting to see these examples.

[00:22:33] But they are sort of one off in a way. And so being able to fill in the blanks around those and make it more broadly applicable is, you know, exactly what everybody wants. I mean, I met with one of my members this week who explained an optimization type problem that he was being asked to develop for a transportation type application. It was very exciting sounding. And if it works, I think it will be one of those examples.

[00:22:59] And he was insisting, and I think this is really where we are today, that the industry really needs to find these applications that are repeatable, demonstrable, expandable, transferable, etc. To make the transition to, you know, a full-blown industry and to reach the next phase, get across the valley of death or whatever.

[00:23:20] Yeah. And QEDC, is one of your roles to influence policy and, you know, standards and export controls and that sort of thing? Well, we are here to help. We're educating all of the parts of the ecosystem that are needed in order to be able to grow the industry. And so we do see ourselves as being a voice of the community.

[00:23:48] We've got broad membership and policymakers are very, you know, excited about quantum, but they're not experts. And so that is a role that we play. We have events in Washington for policymakers. We welcome them to engage with us. We had a tech showcase each of the last two years where we brought some of the companies into Capitol Hill,

[00:24:12] like really in the Capitol building with their technology so that people could actually touch it and see that it's real. This isn't just sort of vaporware. And so there are a lot of different ways that we reach out and help policymakers to understand the importance. And often they have heard, you know, seen the headlines and they're not that aware of the breadth of quantum. So we're always keen to help fill in those blanks.

[00:24:40] And I've had the opportunity to testify to Congress a couple of times. So we play that role where we're sort of the voice of the industry in those kinds of settings. Yeah. Is it too early for enterprises to start training up workforce so that they have muscle on site when quantum eventually hits? Well, it depends on the company, of course.

[00:25:06] I think that whoever in an organization is responsible for sort of strategic thinking, looking a little bit out over the horizon, maybe three years or beyond, would be well advised to be thinking about quantum and educating themselves a little bit. And getting involved with an organization like QEDC is a pretty easy way to keep your finger on the pulse.

[00:25:29] We provide all kinds of different content and training and community networking opportunities. So that's one way to educate yourself. You can start by just doing that kind of keeping abreast of things. The next level might be to hire a consultant or somebody. If you don't want to hire somebody full time, you can engage somebody at a more limited scale to help you understand and educate the senior management, perhaps.

[00:25:58] And then finally, actually building up a little bit of in-house capability would be advisable as it becomes apparent that this is something you're going to need to be really prepared for. And I think the reason that now is a good time to start those steps is because this is not a matter of at some point your IT person is going to come in and say,

[00:26:21] OK, boss, this year we've got to port all of our applications onto this quantum computer and deprecate our old systems. That is not how it's going to work because quantum computers are going to be enabling of doing computation in ways that isn't possible now and will allow you to perhaps solve some business problems or think about your future business in ways that are just not even possible today.

[00:26:46] So it's not about doing things kind of faster or better than you are with your old classical system. And some things you'll never stop doing with the classical system. So it's really a matter of having some understanding of the breadth of the types of problems. You mentioned optimization problems, for instance, that you're going to be able to do that you can't do now at all. And so you kind of have to really change the way you think about your business. And so now is a good time to start doing that.

[00:27:14] And I sometimes remind that on the workforce side, there's a lot of interest in growing the workforce talent pool. And everybody is we are doing that just investing on the government side in more research at universities, educate more people because the students are there getting their education and training. So we're growing this talent pool, just like every company has somebody who's sort of the IT guy or office.

[00:27:40] And in the future, it's likely that most companies are going to have to be having that IT department understand quantum computing. And that's a much bigger talent pool need than just the people who go to work for quantum companies. Right. So I think, again, starting to think about that now is a wise strategy. Yeah. Another thing I see are cloud companies offering quantum computers in the cloud. What are those being used for?

[00:28:08] Well, it is possible to run programs on a quantum computer via the cloud. And there are some cloud platforms that quantum computer companies have signed on to provide that access. I think it's a great way for users to to find their way onto quantum computers. Some companies, I'll just use IBM as an example. They've been at it for quite a long time.

[00:28:34] Put quantum computers on their IBM hosted platform and people can go and learn how to program a quantum computer and to do some sort of toy problems, little problems that you could also do on a classical computer. So it's not an advantage, but you can learn today. Some companies have chosen rather than provide the tech support and do it there themselves to do that through a platform like Bracket or Azure that are run by, I believe, AWS and Microsoft in those cases.

[00:29:02] There are multiple ways for companies to get access to these systems and to start that sort of learning process. Yeah. And the quantum hardware that's available through the cloud is does that tend to be one? I mean, there are all these different flavors of quantum, you know, there's trapped ion and photon and annealing. And I mean, at one point, it's like learning Chinese.

[00:29:28] I had it all in my head, but you very quickly forget it if you're not in the industry. Is there sort of a dominant model that's available through the cloud or is this something that companies can play around with to figure out which? Definitely. There's multiple modes and each type of qubit. So it's sort of like we haven't come down to using just one type of transistor like we have in consumer electronics. The silicon transistor is the one that's in everything.

[00:29:58] We have different kinds of qubit transistors and they each have advantages and disadvantages. And so there are we don't know today whether a single modality will be the dominant one and all the others will sort of go away or whether there may be reasons for keeping multiple types of quantum computers for different kinds of applications.

[00:30:23] There's even a program that is aimed at perhaps building a hybrid quantum system with multiple types of quantum transistors in it. So right now, it's really very much early days in terms of the technology under the hood. Now, there are companies that are building the sort of middleware that allows you to program at once and run on perhaps multiple platforms or be able to through those middleware layers,

[00:30:50] that sort of intermediate representation or whatever, to be able to program and run on different platforms to learn. Again, it's at the sort of experimentation stage. So it's very much I don't know how many decades you have to go back to compare it to classical computing. But we don't have really the integrated circuit yet, although we're getting more sophisticated in that area.

[00:31:12] The company that I visited earlier this week had big, you know, sort of not yet fully integrated systems because they're still in development, although they had several generations of their quantum computers in the lab. And I think those were the quantum computers that when you access it via the cloud, you get to use. So it's just very dynamic. And that's another reason why cloud access kind of makes sense today, because the next generation is going to come very quickly.

[00:31:40] And so, you know, why would an enterprise buy a quantum computer now when it could be superseded very quickly? Yeah, well, and that was another question you talked about in your report about some companies are thinking about on-premise, and there are people buying D-Waves, I mean, presumably for research.

[00:32:01] But why would a company invest in an on-premise quantum computer unless they're building quantum computers? Well, I think that for some organizations, perhaps sort of for security reasons, you know, a big financial institution, pretty deep pockets. They can afford to buy something that they own and control and have less concern about any security issues that might result from operating via the cloud.

[00:32:30] Just one example, perhaps also government users may have similar security reasons for wanting their own system. In our survey, we are often asking, two years from now, what do you think? And so some of these data points that I'm referring to that, you know, on-premise growing, it's still less than the cloud access answer, but that's looking ahead even two years from now. So we'll see if those predictions are, you know, accurate when the time comes.

[00:32:58] What is the link between quantum and AI, and how can quantum accelerate AI? So I'm going to take your question and also flip it around, because right now AI is ahead of quantum in some ways, of course. So what is the nexus of quantum and AI? And I do want to sort of start by dispelling any misunderstanding, because sometimes somebody will come up to me and say, well, now we've got AI, so we don't need quantum computers, right? And the answer is no.

[00:33:28] It's going to be quantum and AI in all likelihood, or quantum for some things and AI for others. We did do a report at QEDC that you can find on our public website under publications on the intersection of quantum and AI. And because AI is a little bit more mature, I would say the AI for quantum answers maybe were more firm in some ways, and how quantum will help AI may be a little less certain.

[00:33:55] But we have sort of ideas on both of those sides. When it comes to how, and then there's the quantum plus AI, what does that look like and mean? So AI is useful today for programming and for doing certain kind of development design, architecture, development work in classical computing. And we have every reason to think it will have similar utility in quantum computing. So that might be one thing.

[00:34:22] There is also, and you may have seen this news a week or so ago, the Genesis mission, which is this program that the U.S. government has rolled out to really promote using AI for R&D, for research and development and scientific discovery. And they announced last week 26 what they called national science and technology challenges that AI could help to address. And two of them were specific to quantum computing. So one was called discovering quantum algorithms.

[00:34:52] So this is really to this point that we talked about earlier. We need to explore and discover quantum algorithms. And AI could be very helpful in doing that more quickly. The second one of these challenges was realizing quantum systems. So this gets to the idea that being able to design, being able to reduce the noisiness, being able to come up with better error correction schemes, all of those kinds of areas of research might be helped by AI.

[00:35:20] And so those are examples of where quantum computing and AI are likely to run into each other or help each other. Quantum computers, often people will ask me, well, is quantum computing going to somehow reduce the amount of energy that AI uses? And I'm not going to promise that. Quantum computers may be able to do some things more efficiently than the current way that it's done.

[00:35:43] And so there could be some energy benefit of the sort of fusion of quantum processing units, along with GPUs and classical CPUs into these AI data centers. But there also could be uses of quantum computing to somehow develop data that's useful for training or improve machine learning and some of the processes where AI doesn't have enough data or doesn't do a very good job.

[00:36:12] And so those are areas where quantum computing might help. But again, quantum computers are not really ready for that yet. So something that the developers probably have in mind as they're thinking about what to use quantum computers for. And AI would be one of the application areas or use case areas. Yeah. Is the market still growing and the new companies are entering this market, both on the hardware and the software side?

[00:36:40] So I would say yes and no. So yes, in the sense that there are a lot of really innovative ideas and there's so much research going on. And so there are constantly new companies that are spinning out from research groups, both at national labs and at universities. And so I see a continuing growth of new businesses. And those some of them are very sort of specific piece of hardware or software.

[00:37:11] It's, you know, to be determined. People, I think, look at the big guys who are investing in quantum and they have very deep pockets, of course. But there are no really big quantum companies. These are big companies that have a little quantum startup inside in a way. And so there's still a lot of room for competition, innovation and for a smaller company, frankly, to win the race. There's also some consolidation happening.

[00:37:37] So there are acquisitions being made and you do read in the business section about those. So in some ways, that's why I say it's both. You have new companies coming along all the time, but you also have already some consolidation happening among the existing companies. And I think that's probably to be expected. So it's interesting times. Yeah. Yeah. That was going to be my next question is whether there is consolidation or whether, you know,

[00:38:08] once somebody has a practical quantum computer, whether it'll sort of collapse the market around one model. Now, I want to add one thing to my answer. And that is on the investor front. I am seeing a growing interest on the part of the investor community in quantum. And I think that speaks to, again, the fact that the horizon is now within that three to five year kind of timeframe,

[00:38:36] which investors are willing to take a risk. And so there's, I think, more and more capital being deployed into this market. And that's another opportunity for us to educate, frankly, at QEDC, because those investors are not always deep subject matter experts in quantum. And so, you know, they can educate themselves by being involved with communities like QEDC. But there's, I've thought for a long time, there's not a shortage of capital.

[00:39:04] And now as the AI sort of investment is maybe flattening out a little bit, there's probably more bandwidth and maybe dollars available to go to quantum companies. So I think for a lot of different reasons, investment trends are likely to go up in this year and the next few years. Yeah.

[00:39:23] Jumping ahead to 2030, what forecasts do you think are overly, will turn out to have been overly conservative or overly optimistic? I mean, just on the timeline, it's people are getting excited. They're starting to gear up. There's more companies entering the market.

[00:39:44] But as you said, it could easily hit a winter if milestones don't get met. Well, I think that there's a saying, I forget who said it. Maybe it was Yogi Berra. No, I think there is a saying that often when it comes to new technologies, there's a greater expectation in the near term and an under expectation of the long term impact.

[00:40:11] And I think quantum is going to be very much among those types of technologies. And so in the sort of four year time frame, looking back from 2030, we might say, well, we were a little bit over optimistic in how widely this would be used in every field for that does computing, which is pretty much every sector.

[00:40:32] But I think this utility for research purposes in all kinds of field, whether it's biomedicine or chemistry or materials or, you know, new types of complex systems or being able to optimize anything from the electric grid to delivery trucks or whatever.

[00:40:54] I think there's going to be a lot of progress in in all of these areas in ways that show that there's not going to be a quantum winter so much as just that progression. And the how the x-axis looks in terms of the years and timeline, that's always hard to pin down. It'll come into focus over time. It'll become clearer and clearer. I think that it's just a question of when, not if at this point. Yeah.

[00:41:24] Okay. I'm kind of running out of questions. Is there anything I haven't touched on that you wanted to say? I'll add that we've been focusing rightly on a lot of the sort of commercial uses and development. The government does play a really important role right now because it is fairly early stage. There's a lot of de-risking that needs to be done. And the government passed this National Quantum Initiative Act in 2018.

[00:41:53] There's a reauthorization bill that's working its array through Congress. There's been some rumors of some executive action, maybe an executive order coming soon. So something to keep your eyes out for because I think those signals are really important to the private sector as well, that this is something that is, it is real and that there is platform and I guess foundational investment being made by the governments.

[00:42:21] And not just by the U.S. government, mind you, by governments all around the world. And so it's important for those dollars to continue to flow. So because, as I said, when you invest in basic research, you're training the workforce. And all of the companies are looking for these incredibly rare quantum workers. And we need more of those.

[00:42:45] And I guess to anybody in your audience who knows a young person who's getting into a STEM discipline, encourage them to look at quantum. It's an exciting new field. This episode is brought to you by Tasty Trade. On Eye on AI, we talk a lot about how artificial intelligence is changing how people analyze information, spot patterns, and make more informed decisions. Markets are no different.

[00:43:13] The edge increasingly comes from having the right tools, the right data, and the ability to understand risk clearly. That's one of the reasons I like what Tasty Trade is building. With Tasty Trade, you can trade stocks, options, futures, and crypto all in one platform with low commissions, including zero commissions on stocks and crypto, so you keep more of what you earn.

[00:43:42] The platform is packed with advanced charting tools, backtesting, strategy selection, and risk analysis tools that help you think in probabilities rather than guesses. They've also introduced an AI-powered search feature that can help you discover symbols aligned with your interests, which is a smart way to explore markets more intentionally.

[00:44:08] For active traders, there are tools like Active Trader Mode, one-click trading, and smart order tracking. And if you're still learning, Tasty Trade offers dozens of free educational courses, plus live support from their trade desk reps during trading hours. If you're serious about trading in a world increasingly shaped by technology, check out Tasty Trade.

[00:44:37] Visit tastytrade.com to start your trading journey today. I'm going to myself. Tasty Trade Inc. is a registered broker-dealer and member of FINLA, NFA, and SIPC.